Can Stephen Curry really know?-Conscious access to outcome prediction of motor actions.

The NBA player Stephen Curry has a habit of turning away from the basket right after taking three-point shots even before the ball reaches the basket, suggesting that he can reliably predict whether the just released shot will hit or not. In order to use this "knowledge" to deliberately decide which action to take next, Stephen Curry needs conscious access to the results of internal processes of outcome prediction and valuation. In general, computational simulations and empirical data suggest that the quality of such internal predictions is related to motor skill level. Whether the results of internal predictions can reliably be consciously accessed, however, is less clear. In the current study, 30 participants each practiced a virtual goal-oriented throwing task for 1000 trials. Every second trial, they were required to verbally predict the success of the current throw. Results showed that on average, verbal prediction accuracy was above an individually computed chance level, taking into account individual success rates and response strategies. Furthermore, prediction accuracy was related to task skill level. Participants with better performances predicted the success of their throws more accurately than participants with poorer performances. For the poorer performing individuals, movement execution was negatively affected when the verbalized predictions were required. They also showed no noticeable modulation of speech characteristics (response latency) for correct and incorrect predictions as observed in the high performers.

Focused Review on Neural Correlates of Different Types of Motor Errors and Related Terminological Issues.

The Error-related negativity (Ne/ERN) and the feedback-related negativity (FRN), two event-related potentials in electroencephalogram tracings, have been used to examine error processing in conscious actions. In the classical terminology the Ne/ERN and the FRN are differentiated with respect to whether internal (Ne/ERN) or external (FRN) error information is processed. In motor tasks, however, errors of different types can be made: A wrong action can be selected that is not adequate to achieve the task goal (or action effect), or the correctly selected action can be mis-performed such that the task goal might be missed (movement error). Depending on the motor task and the temporal sequences of these events, internal and external error information can coincide. Hence, a clear distinction of the information source is difficult, and the classical terminology that differentiates the Ne/ERN and the FRN with respect to internal and external error information becomes ambiguous. But, a stronger focus on the characteristics of the definition of "task" and the cause of "errors", as well as on temporal characteristics of event-related potentials with respect to the task action allows separate examination of the processing of movement errors, the processing of the prediction of action effect errors, or the processing of the detection of action effect errors. The present article gives an overview of example studies investigating the Ne/ERN and the FRN in motor tasks, classifies them with respect to action effect errors or movement errors, and proposes updated terminology.

Valence-dependent brain potentials of processing augmented feedback in learning a complex arm movement sequence.

ERPs in the EEG were scrutinized in learning a complex arm movement sequence with the aim to examine valence effects on processing augmented feedback during practice. Twenty-four healthy subjects practiced one session with 192 feedback trials according to an adaptive bandwidth feedback approach with a high informational level of feedback information (i.e., amplitude and direction of errors). The bandwidth for successful performance (increase of a score for a monetary competition) was manipulated to yield a success rate (positive feedback frequency) of approximately 50% adaptive to the current performance level. This allowed a variation of feedback valence unconfounded by success rate. In line with our hypotheses, the EEG data showed a valence-dependent feedback-related negativity (FRN) and a later fronto-central component at the FCz electrode as well as a P300 component at the Pz electrode. Moreover, the P300 and amplitudes in the FRN time window reduced in the second half of practice but were still dependent on feedback valence. Behavioral adjustments were larger after feedback with negative valence and were predicted by the late fronto-central component. The data support the assumption of feedback valence-dependent modulation of attentional cognitive involvement in motor control and learning.

Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning.

Error processing is an important aspect of learning. The detection and online correction of an error as well as error-based adaptation of subsequent movements enables humans to improve behavior. For this improvement, it is necessary to differentiate between relevant and irrelevant errors. Behavioral adaptations are only reasonable when an error is attributed to one's own behavior and therefore regarded as relevant for subsequent adjustments, whereas irrelevant errors caused by unsystematic external influences should be disregarded. Here, we ask whether error predictions as indexed by the error-related negativity (Ne/ERN) can be used to differentiate relevant and irrelevant errors in movements with a complex visuomotor mapping. Using event-related potentials, we compared the neural activation between relevant (self-induced/internal) errors and irrelevant (externally manipulated) errors in a virtual goal-oriented throwing task. Results show that the Ne/ERN responds more strongly to self-induced errors, while the feedback-related negativity (FRN) more strongly correlates with externally manipulated errors. Moreover, subsequent behavioral adjustments were larger in the relevant compared to the irrelevant error trials. We conclude that predictive processes, marked by the Ne/ERN, can subserve error attribution in naturalistic, complex visuomotor tasks like throwing.

Inference statistical analysis of continuous data based on confidence bands-Traditional and new approaches.

In the analysis of continuous data, researchers are often faced with the problem that statistical methods developed for single-point data (e.g., t test, analysis of variance) are not always appropriate for their purposes. Either methodological adaptations of single-point methods will need to be made, or confidence bands are the method of choice. In this article, we compare three prominent techniques to analyze continuous data (single-point methods, Gaussian confidence bands, and function-based resampling methods to construct confidence bands) with regard to their testing principles, prerequisites, and outputs in the analysis of continuous data. In addition, we introduce a new technique that combines the advantages of the existing methods and can be applied to a wide range of data. Furthermore, we introduce a method enabling a priori and a posteriori power analyses for experiments with continuous data.

Brain negativity as an indicator of predictive error processing: the contribution of visual action effect monitoring.

The error (related) negativity (Ne/ERN) is an event-related potential in the electroencephalogram (EEG) correlating with error processing. Its conditions of appearance before terminal external error information suggest that the Ne/ERN is indicative of predictive processes in the evaluation of errors. The aim of the present study was to specifically examine the Ne/ERN in a complex motor task and to particularly rule out other explaining sources of the Ne/ERN aside from error prediction processes. To this end, we focused on the dependency of the Ne/ERN on visual monitoring about the action outcome after movement termination but before result feedback (action effect monitoring). Participants performed a semi-virtual throwing task by using a manipulandum to throw a virtual ball displayed on a computer screen to hit a target object. Visual feedback about the ball flying to the target was masked to prevent action effect monitoring. Participants received a static feedback about the action outcome (850 ms) after each trial. We found a significant negative deflection in the average EEG curves of the error trials peaking at ~250 ms after ball release, i.e., before error feedback. Furthermore, this Ne/ERN signal did not depend on visual ball-flight monitoring after release. We conclude that the Ne/ERN has the potential to indicate error prediction in motor tasks and that it exists even in the absence of action effect monitoring.NEW & NOTEWORTHY In this study, we are separating different kinds of possible contributors to an electroencephalogram (EEG) error correlate (Ne/ERN) in a throwing task. We tested the influence of action effect monitoring on the Ne/ERN amplitude in the EEG. We used a task that allows us to restrict movement correction and action effect monitoring and to control the onset of result feedback. We ascribe the Ne/ERN to predictive error processing where a conscious feeling of failure is not a prerequisite.

Neural correlates of error prediction in a complex motor task.

The goal of the study was to quantify error prediction processes via neural correlates in the Electroencephalogram (EEG). Access to such a neural signal will allow to gain insights into functional and temporal aspects of error perception in the course of learning. We focused on the error negativity (Ne) or error-related negativity (ERN) as a candidate index for the prediction processes. We have used a virtual goal-oriented throwing task where participants used a lever to throw a virtual ball displayed on a computer monitor with the goal of hitting a virtual target as often as possible. After one day of practice with 400 trials, participants performed another 400 trials on a second day with EEG measurement. After error trials (i.e., when the ball missed the target), we found a sharp negative deflection in the EEG peaking 250 ms after ball release (mean amplitude: t = -2.5, df = 20, p = 0.02) and another broader negative deflection following the first, reaching from about 300 ms after release until unambiguous visual knowledge of results (KR; hitting or passing by the target; mean amplitude: t = -7.5, df = 20, p < 0.001). According to shape and timing of the two deflections, we assume that the first deflection represents a predictive Ne/ERN (prediction based on efferent commands and proprioceptive feedback) while the second deflection might have arisen from action monitoring.